Rename project to machine-vision-poc and clarify initial use case

README.md

@@ -1,10 +1,12 @@
-# Carbofol Machine Vision PoC
+# Machine Vision PoC
 
-Explore whether camera images and reproducible illumination can reliably reveal surface defects on a Carbofol sealing membrane during production. Build a reviewable defect catalogue and a small operator interface as the foundation for later software development.
+Explore camera-based inspection of production processes using reproducible illumination and machine vision. Build a reviewable defect catalogue and a small operator interface as the foundation for later software development.
+
+**First use case:** surface inspection of Carbofol sealing membrane. The requirements, hardware estimates and experiments below describe this initial use case; future materials and processes may require different capture configurations, models and acceptance criteria.
 
 **Status:** documentation and source placeholder only. No capture service, trained model, benchmark, validated detection accuracy or deployed application exists in this repository. Hardware has not been selected or purchased as part of this task.
 
-## Requirements from the discussion
+## Initial use case: requirements from the discussion
 
 | Constraint | Current scope |
 |---|---|
@@ -51,7 +53,7 @@ This is an experimental inspection assistant. Machine-stop control and productio
 
 ## Software home
 
-`src/carbofol_inspection/` reserves the Python package. Its README describes intended boundaries. No dependencies, runtime commands or API endpoints are claimed to work yet. Introduce packaging and pinned dependencies with the first executable vertical slice, starting with recorded images before camera integration.
+`src/machine_vision_poc/` reserves the Python package. Its README describes intended boundaries. No dependencies, runtime commands or API endpoints are claimed to work yet. Introduce packaging and pinned dependencies with the first executable vertical slice, starting with recorded images before camera integration.
 
 Keep runtime images, databases, model weights, secrets and generated outputs out of Git. The future application should accept an explicit data root, preferably on SSD and outside the checkout. Git tracks code, configuration examples and documentation; it is not the defect database.
 

docs/architecture.md

@@ -1,6 +1,6 @@
 # Proposed architecture
 
-All components below are a design proposal, not an implemented or measured system.
+All components below are a design proposal, not an implemented or measured system. The shared architecture is intended for multiple machine-vision use cases; the camera geometry, coverage calculations and material experiments here describe the initial Carbofol use case. Keep material-specific settings, labels and model versions in explicit configurations rather than hard-coding them into the application.
 
 ## Cameras and illumination
 

docs/provenance.md

@@ -7,3 +7,5 @@ This is a compact technical synthesis, not a verbatim transcript. Requirements e
 Historical prices remain attributed discussion estimates; no new market research was performed. Prior claims of ample compute capacity, model fit, deterministic AI decisions and subsecond latency were not measurements and are not carried forward as guarantees. Illustrative scores, defect dimensions and UI examples from the chat are not experimental observations.
 
 The queue/error handling, normalized data model, versioned review history, evidence integrity and phase gates elaborate the discussion into a proposed engineering design. They are not existing implementation or user-approved purchasing decisions. No source images, trained weights or actual production datasets were supplied with this task.
+
+The owner subsequently named the project `machine-vision-poc` to allow future use cases beyond Carbofol. Carbofol remains the first documented application; its requirements and historical discussion are preserved. The Python package placeholder is `machine_vision_poc`.